class_docstr: A wrapper around imgaug https://github.com/aleju/imgaug.
  
  Find available augmenters at
  https://imgaug.readthedocs.io/en/latest/source/overview_of_augmenters.html.
  
  Required Keys:
  
  - img
  - gt_polygons (optional for text recognition)
  - gt_bboxes (optional for text recognition)
  - gt_bboxes_labels (optional for text recognition)
  - gt_ignored (optional for text recognition)
  - gt_texts (optional)
  
  Modified Keys:
  
  - img
  - gt_polygons (optional for text recognition)
  - gt_bboxes (optional for text recognition)
  - gt_bboxes_labels (optional for text recognition)
  - gt_ignored (optional for text recognition)
  - img_shape (optional)
  - gt_texts (optional)
  
  Args:
      args (list[list or dict]], optional): The argumentation list. For
          details, please refer to imgaug document. Take
          args=[['Fliplr', 0.5], dict(cls='Affine', rotate=[-10, 10]),
          ['Resize', [0.5, 3.0]]] as an example. The args horizontally flip
          images with probability 0.5, followed by random rotation with
          angles in range [-10, 10], and resize with an independent scale in
          range [0.5, 3.0] for each side of images. Defaults to None.
      fix_poly_trans (dict): The transform configuration to fix invalid
          polygons. Set it to None if no fixing is needed.
          Defaults to dict(type='FixInvalidPolygon').
docstr: Transform the image and annotation data.
  
  Args:
      results (dict): Result dict containing the data to transform.
  
  Returns:
      dict: The transformed data.
docstr_tok: ["Transform","the","image","and","annotation","data",".","Args",":","results","(","dict",")",":","Result","dict","containing","the","data","to","transform",".","Returns",":","dict",":","The","transformed","data","."]
code: def transform(self, results: Dict) -> Dict:
      """Transform the image and annotation data.
  
      Args:
          results (dict): Result dict containing the data to transform.
  
      Returns:
          dict: The transformed data.
      """
      # img is bgr
      image = results["img"]
      aug = None
      ori_shape = image.shape
  
      if self.augmenter:
          aug = self.augmenter.to_deterministic()
          if not self._augment_annotations(aug, ori_shape, results):
              return None
          results["img"] = aug.augment_image(image)
          results["img_shape"] = (
              results["img"].shape[0],
              results["img"].shape[1],
          )
      if getattr(self, "fix", None) is not None:
          results = self.fix(results)
      return results
  
imports: import warnings
  from typing import Any, Dict, List, Optional, Tuple, Union
  import imgaug
  import imgaug.augmenters
  import numpy
  import torchvision.transforms
  from mmcv.transforms import Compose
  from mmcv.transforms.base import BaseTransform
  from PIL import Image
  from mmocr.registry import TRANSFORMS
  from mmocr.utils import poly2bbox
label_desc: Use image node_id 2 for calling the ImgAugWrapper obj's underlying member method code with example usage: obj.transform(results) and returns: results, None